4 Components of Data-Driven Decision Making
Data plays an important role in how a business operates. The way a business collects manages, stores and acts upon data are crucial determining the effectiveness of any business operation. As a business expands, data-driven decision making becomes increasingly more important to its continued success. It’s not uncommon for a small business to underestimate the power of data, and as a result, fail to grow or succeed.
The amount of actionable information contained within collected data is substantial, and this data can be used to effectively make intelligent business decisions when correctly managed. In order to effectively analyze data for use, it’s important to employ analytical strategies and have an end goal in mind. Large sums of data can be overwhelming, but the vast amount of valuable actionable possibilities contained within make analysis critical to data-driven decision making.
Data-driven decision making requires a multifaceted approach, and below we’ll examine several aspects of the process in order to better understand how to implement it into a business strategy.
1. Big Data Management
Although large quantities of data can provide extensive and valuable information, it must first be organized and made manageable. Big data is typically characterised by high velocity, high volume, and wide variety. Data accumulates fast and in large volume, and in order to tap into it, it has to be organized by variety and according to analytical strategy.
Correctly organizing, storing and directing collected data to the right analytical departments is key in order to extract useful information from within.
2. Data Collection
In order to take advantage of data-driven decision making and the power of big data, you must first have data to work with. Data is collected in a variety of ways such as customer purchases, services used, in which areas business is doing well versus which areas aren’t performing well enough. There are countless possible sources, and a business is likely to tap into a multitude of them.
As data is collected, it typically isn’t immediately actionable and must be stored safely for future analysis in order to extract the actionable data. Big data is nearly universally stored digitally, in order to cut costs and improve accessibility. This means that in order to manage big data, dedicated storage facilities must be available, as data can quickly accumulate into quantities measured in terabytes or even petabytes.
3. Data Analysis
Upon collecting, storing and organizing data, a business can begin analyzing the data. Data analysis is most often performed by dedicated and specialized software and systems. These systems are instructed to analyze the data in a variety of ways to produce actionable data for various departments.
Data analysis is most often automated to a large degree, as large sums of data are quite simply extremely difficult and incredibly costly to analyze manually. Data analysis not only produces actionable data for a variety of business departments, but it can also be used to create predictive models for business developments, provide wide ranges of statistics, and even attempt to analyze how various business decision could impact the success of a business.
4. Business Intelligence
Following analysis, manageable, easily understood data is provided which can then be used to drive intelligent business decisions. Different departments of a single business often utilize the analyzed data in unique ways. Executives can then use the data to develop effective new business strategies and discover untapped business opportunities exposed by the analyzed data.
Those are just a few of the key steps in achieving a system whereby informed and data-driven decisions can be made. Different entities often approach big data in unique ways, but its value is undeniable. Don’t let your data slip through your hands, deploy a data management strategy and reap the benefits.